This is a Pytorch implementation of gan_64x64.py
from Improved Training of Wasserstein GANs.
- Python >= 3.6
- Pytorch v0.4.0
- Numpy
- SciPy
- tensorboardX (installation here). It is very convenient to see costs and results during training with TensorboardX for Pytorch
- TensorFlow for tensorboardX
gan_train.py
: This model is mainly based onGoodGenerator
andGoodDiscriminator
ofgan_64x64.py
model from Improved Training of Wasserstein GANs. It has been trained on LSUN dataset for around 100k iters.congan_train.py
: ACGAN implementation, trained on 4 classes of LSUN dataset
Sample 1 | Sample 2 |
---|---|
- dining_room: 1
- bridge: 2
- restaurant: 3
- tower: 4
Sample 1 | Sample 2 |
---|---|
During the implementation of this model, we built a test module to compare the result between original model (Tensorflow) and our model (Pytorch) for every layer we implemented. It is available at compare-tensorflow-pytorch
Results such as costs, generated images (every 200 iters) for tensorboard will be written to ./runs
folder.
To display the results to tensorboard, run: tensorboard --logdir runs